Non stationary motor fault detection using recent quadratic time frequency representations

Abstract—As the use of electric motors increases in the aerospace and transportation industries where operating condi-tions continuously change with time, fault detection in electric motors has been gaining importance. Motor diagnostics in a non-stationary environment is difficult and often needs sophisticated signal processing techniques. In recent times, a plethora of new time–frequency distributions has appeared, which are inherently suited to the analysis of nonstationary signals while offering supe-rior frequency resolution characteristics. The Zhao–Atlas–Marks distribution is one such distribution. This paper proposes the use of these new time–frequency distributions to enhance nonstation-ary fault diagnostics in electric motors. One common myth has been that the quadratic time–frequency distributions are not suit-able for commercial implementation. This paper also addresses this issue in detail. Optimal discrete-time implementations of some of these quadratic time–frequency distributions are explained. These time–frequency representations have been implemented on a digital signal processing platform to demonstrate that the pro-posed methods can be implemented commercially. Index Terms—Choi–Williams distribution (CWD), condition monitoring, eccentricity, fault diagnosis, motors, rotor faults, time–frequency analysis, Zhao–Atlas–Marks (ZAM). I